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Feed water treatment process (FWTP) is an essential part of utility boilers; and fault detection is expected for its reliability improvement. Classical principal component analysis (PCA) has been applied to FWTPs in our previous work; however, the noises of T2 and SPE statistics result in false detections and missed detections. In this paper, Wavelet denoise (WD) is combined with PCA to form a new algorithm, (PCA WD), where WD is intentionally employed to deal with the noises. The parameter selection of PCAWD is further formulated as an optimization problem; and PSO is employed for optimization solution. A FWTP, sustaining two 1000 MW generation units in a coalﬁred power plant, is taken as a study case. Its operation data is collected for following veriﬁcation study. The results show that the optimized WD is effective to restrain the noises of T2 and SPE statistics, so as to improve the performance of PCAWD algorithm. And, the parameter optimization enables PCAWD to get its optimal parameters in an auto matic way rather than on individual experience. The optimized PCAWD is further compared with classical PCA and sliding window PCA (SWPCA), in terms of four cases as bias fault, drift fault, broken line fault and normal condition, respectively. The advantages of the optimized PCAWD, against classical PCA and SWPCA, is ﬁnally convinced with the results.
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